Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "10" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 53 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 51 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459868 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.410832 | -0.313683 | 3.543999 | 2.645545 | 4.401208 | 2.972902 | 2.795277 | 1.206921 | 0.6682 | 0.6603 | 0.4084 | nan | nan |
| 2459867 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.039504 | -0.883769 | 2.313034 | 2.366390 | 5.614932 | 1.916598 | 1.261155 | -0.408391 | 0.6853 | 0.6657 | 0.4094 | nan | nan |
| 2459866 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.019965 | -0.603540 | 2.215374 | 2.994577 | 2.269971 | 2.456912 | -0.016273 | -0.511639 | 0.6921 | 0.6681 | 0.4047 | nan | nan |
| 2459865 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.589261 | 0.521942 | 14.039909 | 13.052654 | 6.202671 | 3.435899 | 4.189152 | 1.309642 | 0.7128 | 0.6929 | 0.3773 | nan | nan |
| 2459864 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.942855 | 3.324773 | 19.961072 | 19.370720 | 3.773421 | 5.426571 | -2.482205 | -2.594733 | 0.6876 | 0.6621 | 0.4253 | nan | nan |
| 2459863 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.439147 | 1.367037 | 2.734789 | 2.251424 | 0.665459 | 2.062912 | -2.384099 | -2.526754 | 0.6832 | 0.6554 | 0.4154 | nan | nan |
| 2459862 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 12.650956 | 2.316629 | 24.202251 | 22.931379 | 8.184956 | 8.579861 | -0.428354 | -2.088920 | 0.6627 | 0.6864 | 0.4254 | nan | nan |
| 2459861 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.147452 | 0.621795 | 1.473307 | 0.770012 | 0.299371 | -1.096501 | -2.078696 | -2.461831 | 0.6968 | 0.6694 | 0.4268 | nan | nan |
| 2459860 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.894139 | 1.829972 | 18.466351 | 17.469119 | 6.786616 | 9.519988 | -1.584057 | -2.287632 | 0.6999 | 0.6632 | 0.4256 | nan | nan |
| 2459859 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.491398 | 0.163121 | 1.587275 | 0.971320 | -0.546335 | -0.593183 | -1.972421 | -2.115828 | 0.7085 | 0.6652 | 0.4212 | nan | nan |
| 2459858 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.945252 | 0.254012 | 1.202960 | 0.448469 | 0.494772 | -1.014940 | -2.238617 | -2.694415 | 0.7142 | 0.6759 | 0.4327 | 1.631334 | 1.385217 |
| 2459857 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 23.981273 | 20.614842 | -1.584988 | -1.644935 | 6.616830 | 24.144510 | 0.730085 | 0.755770 | 0.0280 | 0.0286 | 0.0006 | nan | nan |
| 2459856 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.167335 | 2.739213 | 17.113305 | 16.597254 | 2.260160 | 8.449722 | -0.003449 | -2.252530 | 0.7102 | 0.6881 | 0.4200 | 3.316993 | 2.987689 |
| 2459855 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 75.733829 | 75.438506 | inf | inf | 4363.406592 | 4363.252393 | 4164.445122 | 4163.350360 | 0.0113 | 0.0088 | 0.0033 | 0.000000 | 0.000000 |
| 2459854 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.93% | 0.00% | 1.332556 | -1.834806 | -0.419200 | 1.427545 | 1.817472 | 1.090790 | 0.992124 | 1.226959 | 0.7080 | 0.7269 | 0.4472 | 1.430003 | 1.313335 |
| 2459853 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.354939 | -1.398691 | -0.480768 | 2.179571 | -0.024237 | 1.270910 | 0.760993 | 0.567422 | 0.7255 | 0.6655 | 0.4436 | 1.571183 | 1.390008 |
| 2459852 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.679551 | -0.413727 | -0.529298 | 2.088841 | 0.444226 | 9.337319 | 2.430296 | 7.318459 | 0.8104 | 0.8172 | 0.2604 | 4.687730 | 5.195400 |
| 2459851 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459850 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.285726 | -1.455795 | -0.805971 | -0.169680 | 1.779394 | 4.844649 | 2.720462 | 8.484891 | 0.7255 | 0.7341 | 0.3681 | 2.932261 | 2.751693 |
| 2459849 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.67% | 0.00% | -0.216231 | -1.450859 | -0.427428 | -0.934531 | -0.233781 | 2.255605 | -0.179287 | 1.420497 | 0.7263 | 0.7282 | 0.3759 | 1.419392 | 1.317231 |
| 2459848 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 30.15% | 0.00% | 0.097102 | -0.720190 | 0.022576 | 0.770318 | -0.235388 | 2.583331 | 0.620401 | 0.626967 | 0.7019 | 0.7282 | 0.3897 | 1.579200 | 1.411103 |
| 2459847 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.21% | 0.00% | 0.462342 | -1.000419 | 0.105055 | 0.757076 | 0.889975 | 1.148240 | 0.449540 | -0.364151 | 0.7063 | 0.6617 | 0.4471 | 1.659918 | 1.459742 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 0.00% | 0.230012 | -0.866343 | 0.758440 | 0.338094 | -0.249136 | 0.806345 | 0.143336 | -0.111791 | 0.8313 | 0.6624 | 0.5159 | 1.468997 | 1.349597 |
| 2459845 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.02% | 1.66% | -0.658850 | -0.529155 | 0.656920 | -0.569334 | 0.506227 | 1.045100 | 0.057052 | -0.817350 | 0.7275 | 0.7350 | 0.3919 | 1.352881 | 1.301207 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.745060 | 1.043563 | 1.252949 | -0.573878 | 0.611681 | 2.810546 | 0.671851 | -0.602478 | 0.0246 | 0.0248 | 0.0005 | nan | nan |
| 2459843 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 16.310688 | 19.088690 | 93.745743 | 102.415053 | nan | nan | -29.231224 | -30.920487 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.437601 | -0.820176 | -0.883160 | -0.316914 | -0.483368 | 1.126944 | -0.164322 | 0.031494 | 0.0948 | 0.0778 | 0.0200 | 6.922731 | 15.323865 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0353 | 0.0383 | 0.0030 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.517114 | -1.008364 | -0.031023 | -0.975258 | 5.765379 | 7.973955 | 39.721364 | 28.566460 | 0.0330 | 0.0522 | 0.0019 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 3.744096 | 3.508064 | -0.022051 | 0.895690 | 9.732769 | 14.755481 | 37.434030 | 38.311506 | 0.0293 | 0.0315 | 0.0009 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 29.57% | 0.00% | 28.95% | 0.00% | 1.262231 | -1.068565 | -0.773343 | -0.360292 | 0.354087 | 0.847330 | 1.350865 | 1.986729 | 0.7442 | 0.4181 | 0.5882 | 1.706412 | 1.470903 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.361339 | -0.733262 | 1.019125 | 0.024895 | -0.173231 | -0.992856 | 0.722670 | -0.291608 | 0.0324 | 0.0401 | 0.0003 | nan | nan |
| 2459830 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.743747 | -0.610572 | -0.882400 | -0.456120 | -0.509707 | 1.324196 | 0.724170 | 0.300614 | 0.0915 | 0.0649 | 0.0120 | 1.303072 | 1.300018 |
| 2459829 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.231626 | -1.163740 | -0.807659 | -0.140742 | 0.797843 | 0.605320 | 2.287715 | 3.247062 | 0.0893 | 0.0718 | 0.0123 | 36.312933 | 30.211786 |
| 2459828 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 6.617837 | -0.490914 | -0.482961 | -0.036162 | 4.163129 | 3.638256 | 29.765854 | 27.160405 | 0.0845 | 0.0634 | 0.0089 | 12.467905 | 8.747301 |
| 2459827 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.882477 | -1.051993 | -0.396077 | 0.915916 | 1.250097 | 0.802326 | 0.775506 | -0.384740 | 0.0901 | 0.0712 | 0.0131 | 1.242944 | 1.244005 |
| 2459826 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.329814 | -0.656352 | -0.588076 | 0.503748 | 0.274080 | 2.075135 | 2.057956 | 0.997595 | 0.0729 | 0.0725 | 0.0130 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.834176 | -0.179971 | -1.041468 | -0.295205 | 10.566951 | 10.882540 | 4.573496 | 4.188314 | 0.0876 | 0.0699 | 0.0114 | 1.260784 | 1.255267 |
| 2459824 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.765149 | -0.431301 | -0.767454 | 0.229355 | 7.922095 | 7.810270 | 14.674376 | 13.410154 | 0.0995 | 0.0712 | 0.0140 | 0.996788 | 0.993954 |
| 2459823 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.199248 | -1.079663 | -0.561886 | 0.515014 | -0.704675 | 0.625826 | 1.470253 | 0.845615 | 0.0768 | 0.0629 | 0.0138 | 0.982258 | 0.970710 |
| 2459822 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.359093 | -0.883888 | -0.783986 | 0.220872 | -1.003378 | 1.028444 | 0.299527 | -0.730583 | 0.0704 | 0.0704 | 0.0094 | 1.283442 | 1.279007 |
| 2459821 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.340915 | -0.535002 | -0.918776 | 0.407885 | 1.315108 | 1.897746 | 2.158590 | 0.456161 | 0.0882 | 0.0658 | 0.0092 | 1.246942 | 1.246313 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.536045 | -1.231764 | -0.914314 | 0.459514 | -0.326835 | 1.287798 | 1.212201 | 1.852941 | 0.7397 | 0.6381 | 0.4571 | 1.676047 | 1.473883 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.586074 | -0.906545 | -1.013075 | 0.159513 | -0.331437 | 0.392736 | 1.713643 | 1.268407 | 0.7827 | 0.5996 | 0.5380 | 1.681469 | 1.597135 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.139031 | -0.764777 | -1.162512 | -0.008904 | -0.351031 | 0.260739 | 2.515742 | 0.217801 | 0.8344 | 0.5560 | 0.6295 | 1.834214 | 1.607911 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.535116 | -0.674818 | -1.090450 | -0.165912 | -0.449308 | -0.609965 | 3.356802 | 0.519143 | 0.7700 | 0.6045 | 0.5438 | 1.882574 | 1.735115 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Shape | 23.410832 | -0.313683 | 23.410832 | 2.645545 | 3.543999 | 2.972902 | 4.401208 | 1.206921 | 2.795277 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Shape | 8.039504 | 8.039504 | -0.883769 | 2.313034 | 2.366390 | 5.614932 | 1.916598 | 1.261155 | -0.408391 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Power | 2.994577 | -0.603540 | 0.019965 | 2.994577 | 2.215374 | 2.456912 | 2.269971 | -0.511639 | -0.016273 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Power | 14.039909 | 7.589261 | 0.521942 | 14.039909 | 13.052654 | 6.202671 | 3.435899 | 4.189152 | 1.309642 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Power | 19.961072 | 3.324773 | 5.942855 | 19.370720 | 19.961072 | 5.426571 | 3.773421 | -2.594733 | -2.482205 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Shape | 3.439147 | 3.439147 | 1.367037 | 2.734789 | 2.251424 | 0.665459 | 2.062912 | -2.384099 | -2.526754 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Power | 24.202251 | 12.650956 | 2.316629 | 24.202251 | 22.931379 | 8.184956 | 8.579861 | -0.428354 | -2.088920 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Shape | 3.147452 | 0.621795 | 3.147452 | 0.770012 | 1.473307 | -1.096501 | 0.299371 | -2.461831 | -2.078696 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Power | 18.466351 | 2.894139 | 1.829972 | 18.466351 | 17.469119 | 6.786616 | 9.519988 | -1.584057 | -2.287632 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Power | 1.587275 | 0.491398 | 0.163121 | 1.587275 | 0.971320 | -0.546335 | -0.593183 | -1.972421 | -2.115828 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Shape | 2.945252 | 0.254012 | 2.945252 | 0.448469 | 1.202960 | -1.014940 | 0.494772 | -2.694415 | -2.238617 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 24.144510 | 20.614842 | 23.981273 | -1.644935 | -1.584988 | 24.144510 | 6.616830 | 0.755770 | 0.730085 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Power | 17.113305 | 3.167335 | 2.739213 | 17.113305 | 16.597254 | 2.260160 | 8.449722 | -0.003449 | -2.252530 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Power | inf | 75.438506 | 75.733829 | inf | inf | 4363.252393 | 4363.406592 | 4163.350360 | 4164.445122 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Variability | 1.817472 | -1.834806 | 1.332556 | 1.427545 | -0.419200 | 1.090790 | 1.817472 | 1.226959 | 0.992124 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Power | 2.179571 | -1.398691 | 0.354939 | 2.179571 | -0.480768 | 1.270910 | -0.024237 | 0.567422 | 0.760993 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 9.337319 | 2.679551 | -0.413727 | -0.529298 | 2.088841 | 0.444226 | 9.337319 | 2.430296 | 7.318459 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Discontinuties | 8.484891 | 0.285726 | -1.455795 | -0.805971 | -0.169680 | 1.779394 | 4.844649 | 2.720462 | 8.484891 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 2.255605 | -0.216231 | -1.450859 | -0.427428 | -0.934531 | -0.233781 | 2.255605 | -0.179287 | 1.420497 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 2.583331 | -0.720190 | 0.097102 | 0.770318 | 0.022576 | 2.583331 | -0.235388 | 0.626967 | 0.620401 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 1.148240 | -1.000419 | 0.462342 | 0.757076 | 0.105055 | 1.148240 | 0.889975 | -0.364151 | 0.449540 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 0.806345 | 0.230012 | -0.866343 | 0.758440 | 0.338094 | -0.249136 | 0.806345 | 0.143336 | -0.111791 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 1.045100 | -0.529155 | -0.658850 | -0.569334 | 0.656920 | 1.045100 | 0.506227 | -0.817350 | 0.057052 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 2.810546 | -0.745060 | 1.043563 | 1.252949 | -0.573878 | 0.611681 | 2.810546 | 0.671851 | -0.602478 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Power | 102.415053 | 19.088690 | 16.310688 | 102.415053 | 93.745743 | nan | nan | -30.920487 | -29.231224 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Shape | 1.437601 | -0.820176 | 1.437601 | -0.316914 | -0.883160 | 1.126944 | -0.483368 | 0.031494 | -0.164322 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Discontinuties | 39.721364 | -1.008364 | 0.517114 | -0.975258 | -0.031023 | 7.973955 | 5.765379 | 28.566460 | 39.721364 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Discontinuties | 38.311506 | 3.508064 | 3.744096 | 0.895690 | -0.022051 | 14.755481 | 9.732769 | 38.311506 | 37.434030 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Discontinuties | 1.986729 | 1.262231 | -1.068565 | -0.773343 | -0.360292 | 0.354087 | 0.847330 | 1.350865 | 1.986729 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Power | 1.019125 | 0.361339 | -0.733262 | 1.019125 | 0.024895 | -0.173231 | -0.992856 | 0.722670 | -0.291608 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 1.324196 | -1.743747 | -0.610572 | -0.882400 | -0.456120 | -0.509707 | 1.324196 | 0.724170 | 0.300614 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Discontinuties | 3.247062 | -1.163740 | 3.231626 | -0.140742 | -0.807659 | 0.605320 | 0.797843 | 3.247062 | 2.287715 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Discontinuties | 29.765854 | -0.490914 | 6.617837 | -0.036162 | -0.482961 | 3.638256 | 4.163129 | 27.160405 | 29.765854 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Variability | 1.250097 | -0.882477 | -1.051993 | -0.396077 | 0.915916 | 1.250097 | 0.802326 | 0.775506 | -0.384740 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 2.075135 | -0.656352 | -1.329814 | 0.503748 | -0.588076 | 2.075135 | 0.274080 | 0.997595 | 2.057956 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 10.882540 | -0.179971 | -0.834176 | -0.295205 | -1.041468 | 10.882540 | 10.566951 | 4.188314 | 4.573496 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Discontinuties | 14.674376 | 1.765149 | -0.431301 | -0.767454 | 0.229355 | 7.922095 | 7.810270 | 14.674376 | 13.410154 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Discontinuties | 1.470253 | -1.079663 | 0.199248 | 0.515014 | -0.561886 | 0.625826 | -0.704675 | 0.845615 | 1.470253 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Variability | 1.028444 | -0.359093 | -0.883888 | -0.783986 | 0.220872 | -1.003378 | 1.028444 | 0.299527 | -0.730583 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Discontinuties | 2.158590 | -0.535002 | 0.340915 | 0.407885 | -0.918776 | 1.897746 | 1.315108 | 0.456161 | 2.158590 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Temporal Discontinuties | 1.852941 | -1.536045 | -1.231764 | -0.914314 | 0.459514 | -0.326835 | 1.287798 | 1.212201 | 1.852941 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Discontinuties | 1.713643 | -1.586074 | -0.906545 | -1.013075 | 0.159513 | -0.331437 | 0.392736 | 1.713643 | 1.268407 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Discontinuties | 2.515742 | -0.764777 | 0.139031 | -0.008904 | -1.162512 | 0.260739 | -0.351031 | 0.217801 | 2.515742 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | ee Temporal Discontinuties | 3.356802 | -0.674818 | -0.535116 | -0.165912 | -1.090450 | -0.609965 | -0.449308 | 0.519143 | 3.356802 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | N02 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |